Practical Machine Learning: How To Decide What Really Matters

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Big data is beginning to make a big difference when it comes to building machine learning systems. Moreover, many of the methods for building these systems don’t require years of study before you can apply them. In this talk, I will present examples of key principles that really matter in making choices about data, algorithms and architecture when you build practical learning systems.

Ted Dunning is Chief Application Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects . Ted has been very active in mentoring new Apache projects and is currently serving as vice president of incubation for the Apache Software Foundation . Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems. He built fraud detection systems for ID Analytics (LifeLock) and he has 24 patents issued to date and a dozen pending. Ted has a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. He also bought the beer at the first Hadoop user group meeting.

In collaboration with GSU, location sponsor for the evening